ThermoLink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction

热稳定性 化学 蛋白质二硫键异构酶 半胱氨酸 共价键 蛋白质折叠 二硫键 组合化学 生物化学 有机化学
作者
Ran Xu,Qican Pan,Guoliang Zhu,Yilin Ye,Minghui Xin,Zechen Wang,Sheng Wang,Weifeng Li,Yanjie Wei,Jingjing Guo,Liangzhen Zheng
出处
期刊:Protein Science [Wiley]
卷期号:33 (9) 被引量:1
标识
DOI:10.1002/pro.5097
摘要

Abstract Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we initially collected disulfide bond and protein thermostability data from extensive literature sources. Thereafter, we extracted various sequence‐ and structure‐based features and constructed machine‐learning models to predict whether disulfide bonds can improve protein thermostability. Among all models, the neighborhood context model based on the Adaboost‐DT algorithm performed the best, yielding “area under the receiver operating characteristic curve” and accuracy scores of 0.773 and 0.714, respectively. Furthermore, we also found AlphaFold2 to exhibit high superiority in predicting disulfide bonds, and to some extent, the coevolutionary relationship between residue pairs potentially guided artificial disulfide bond design. Moreover, several mutants of imine reductase 89 (IR89) with artificially designed thermostable disulfide bonds were experimentally proven to be considerably efficient for substrate catalysis. The SS‐bond data have been integrated into an online server, namely, ThermoLink, available at guolab.mpu.edu.mo/thermoLink .
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